Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

Khan, Subhan; Guivant, José

doi:10.1038/s41598-022-16226-y

Cited by 13 publications

(4 citation statements)

References 44 publications

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“…Thus, this paper contributes to the safe evacuations of people incapacitated to walk, or animals or goods, through the use of a UGV, which will drive them to a safe destination based on the CTG function (which considers all of those safe goals) and on other factors present in the context of operation. Finally, in contrast to our previous work on MPC [42,43], the main contributions of this paper are as follows:…”

Section: Contributionsmentioning

confidence: 99%

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Khan¹,

Guivant²,

Li³

et al. 2023

Preprint

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Autonomous vehicles can be used for evacuations, particularly for assisting disabled people; they can also be used for moving animals and goods. In those cases, the platform must deal with partially known infrastructure and other context characteristics, including dynamic obstacles. The destination may not be unique, as multiple safe destinations may be feasible. This paper proposes a hybrid model predictive control (HMPC) approach for performing trajectory optimization. The approach exploits the available global cost-to-go (CTG) function and a model of the platform dynamics, in addition to awareness information of the nearby context. The CTG function implicitly allows the selection from multiple destinations in the context of operation. First, the Global 2D CTG is generated and periodically updated at low frequency using, in this case, pseudo priority queues (PPQ) based Dijkstra algorithm, which can generate optimal CTG functions of large maps, requiring low processing cost. Secondly, an MPC process based on stochastic dynamic programming (SDP), running at high frequency, generates the actual control actions, in real-time, based on the last available CTG information, in combination with a model of the platform dynamics and the perceived description of the surrounding area, including static, dynamic objects, skid-slip effects. Finally, simulations are performed to validate the proposed planning and control scheme.

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Section: Contributionsmentioning

confidence: 99%

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Khan¹,

Guivant²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The ability to handle multiple destinations and the low processing cost for generating the CTG function make this approach suitable for safely evacuating people with disabilities, animals, and goods using a UGV. This work builds on previous research on MPC, and contributes to the development of safe evacuation methods.Finally, in contrast to our previous work on MPC [40,41], the main contributions of this paper are as follows:…”

Section: Contributionsmentioning

confidence: 99%

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Khan¹,

Guivant²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

This paper presents an approach for optimizing trajectories of autonomous vehicles in evacuation scenarios, particularly for the transportation of handicapped individuals, animals, and products. First, the hybrid model predictive control (HMPC) technique takes into account partially known infrastructure and dynamic obstacles, and allows for multiple secure destinations. The approach utilizes a platform-specific dynamics model, a global cost-to-go (CTG) function, and contextual awareness. In addition, the CTG function is generated using a Dijkstra algorithm and pseudo priority queues (PPQ), and allows for efficient trajectory planning on large maps. Furthermore, the control actions are generated in real-time using a high-frequency MPC process based on stochastic dynamic programming (SDP), using the most recent CTG information and a model of the vehicle dynamics and the perceived environment, including static and dynamic objects and skid-slip effects. Finally, the effectiveness of the proposed technique is validated through simulations.

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“…Some existing techniques have used MPC in the presence of wheel slip. For instance, a min-max MPC for agriculture application [32], a traction control problem on stabilizing wheeled mobile robot (WMR) [33], a detailed analysis on controlling off-road vehicles [34], and a voltage control strategy based on MPC and nonlinear disturbance observer (NDO) is implemented in [35]. For industrial-grade applications, it is important to work without colliding with a moving or static object.…”

Section: A Related Workmentioning

confidence: 99%

Design and implementation of proximal planning and control of an unmanned ground vehicle in the dynamic environment

Khan¹,

Guivant²

2022

Preprint

Self Cite

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This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) affected by skidding and slip disturbances. The control approach also considers the presence of moving and static obstacles in the context of operation. The PPC technique is divided into three parts; first, a nonlinear model predictive control (NMPC) based path-planner is designed to periodically generate an updated feasible trajectory to prevent the vehicle from colliding with other objects from start to the goal pose. In particular, a proximal averaged Newton-type method for optimal control (PANOC) is used to design NMPC. Second, evolutionary programming (EP) based kinematic control (KC) is designed to generate the velocity commands. Third, a dynamic control law is proposed with an extended state observer (ESO) to estimate the effects of bounded but unknown perturbations. Finally, simulations are performed in the presence of linear and nonlinear trajectories of moving obstacles (MO) and static obstacles (SO) to verify the performance of the proposed scheme. Additionally, we have investigated and confirmed that the proposed PPC could run in real-time on a CPU with limited resources.

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Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

Cited by 13 publications

References 44 publications

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Hybrid Model Predictive Control for Autonomous Emergency Evacuation Robot

Design and implementation of proximal planning and control of an unmanned ground vehicle in the dynamic environment

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